On the Brown measure of $x + i y$, with $x,y$ selfadjoint and $y$ free Poisson
Franz Lehner, Alexandru Nica, Kamil Szpojankowski, Ping Zhong

TL;DR
This paper develops a method to compute the absolutely continuous part of the Brown measure for sums of freely independent selfadjoint operators, specifically when one has a free Poisson distribution, using matrix-valued subordination functions and a change of variables.
Contribution
It introduces a new parametrization approach for the Brown measure of $x + i y$, leveraging the matrix-valued subordination function and its inverse to derive an explicit density formula.
Findings
Derived an explicit formula for the Brown measure density.
Established conditions under which the formula applies.
Provided a new computational approach for free probability measures.
Abstract
Let be freely independent selfadjoint elements in a -probability space, where has free Poisson distribution of parameter . We pursue a methodology for computing the absolutely continuous part of the Brown measure of , which relies on the matrix-valued subordination function of the Hermitization of , and on the fact that has an explicitly described left inverse . Our main point is that the Brown measure of becomes more approachable when it is reparametrized via a certain change of variable , with open subsets of , where and are defined in terms of the aforementioned left inverse , and contains the support of the Brown measure. More precisely, we find (with some conditions on the distribution of ,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsRandom Matrices and Applications · Markov Chains and Monte Carlo Methods · Holomorphic and Operator Theory
